# A comparative study of attention mechanism and generative adversarial network in facade damage segmentation

Source: [A comparative study of attention mechanism and generative adversarial network in facade damage segmentation](<https://www.sota2.com/research/paper/68fa6d801e209131cea3a1d6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:35:22.648Z

Authors: Fangzheng Lin, Jiesheng Yang, Jiangpeng Shu, Raimar J. Scherer (3) \_\_INSTITUTION\_4\_\_ Institute of Construction Informatics, Dresden University of Technology, (2) Collage of Civil Engineering, Architecture, Zhejiang University, (3) Deep Learning Center, Changzhou Microintelligence Co., Ltd.)

Published: 2022-09-27T09:59:32.000Z

## Abstract

Semantic segmentation profits from deep learning and has shown its possibilities in handling the graphical data from the on-site inspection. As a result, visual damage in the facade images should be detected. Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation. With specific focuses on these two strategies, this paper adopts U-net, a representative convolutional neural network, as the primary network and presents a comparative study in two steps. First, cell images are utilized to respectively determine the most effective networks among the U-nets with attention mechanism or generative adversarial networks. Subsequently, selected networks from the first test and their combination are applied for facade damage segmentation to investigate the performances of these networks. Besides, the combined effect of the attention mechanism and the generative adversarial network is discovered and discussed.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.13283>)

[PDF](<https://arxiv.org/pdf/2209.13283.pdf>)